A Bayesian Committee Machine Potential for Oxygen-containing Organic Compounds
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866929262677721088 |
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| author | Kim, Seungwon Yang, D. ChangMo Willow, Soohaeng Yoo Myung, Chang Woo |
| author_facet | Kim, Seungwon Yang, D. ChangMo Willow, Soohaeng Yoo Myung, Chang Woo |
| contents | Understanding the pivotal role of oxygen-containing organic compounds in serving as an energy source for living organisms and contributing to protein formation is crucial in the field of biochemistry. This study addresses the challenge of comprehending protein-protein interactions (PPI) and developing predicitive models for proteins and organic compounds, with a specific focus on quantifying their binding affinity. Here, we introduce the active Bayesian Committee Machine (BCM) potential, specifically designed to predict oxygen-containing organic compounds within eight groups of CHO. The BCM potential adopts a committee-based approach to tackle scalability issues associated with kernel regressors, particularly when dealing with large datasets. Its adaptable structure allows for efficient and cost-effective expansion, maintaing both transferability and scalability. Through systematic benchmarking, we position the sparse BCM potential as a promising contender in the pursuit of a universal machine learning potential. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_01158 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Bayesian Committee Machine Potential for Oxygen-containing Organic Compounds Kim, Seungwon Yang, D. ChangMo Willow, Soohaeng Yoo Myung, Chang Woo Materials Science Machine Learning Understanding the pivotal role of oxygen-containing organic compounds in serving as an energy source for living organisms and contributing to protein formation is crucial in the field of biochemistry. This study addresses the challenge of comprehending protein-protein interactions (PPI) and developing predicitive models for proteins and organic compounds, with a specific focus on quantifying their binding affinity. Here, we introduce the active Bayesian Committee Machine (BCM) potential, specifically designed to predict oxygen-containing organic compounds within eight groups of CHO. The BCM potential adopts a committee-based approach to tackle scalability issues associated with kernel regressors, particularly when dealing with large datasets. Its adaptable structure allows for efficient and cost-effective expansion, maintaing both transferability and scalability. Through systematic benchmarking, we position the sparse BCM potential as a promising contender in the pursuit of a universal machine learning potential. |
| title | A Bayesian Committee Machine Potential for Oxygen-containing Organic Compounds |
| topic | Materials Science Machine Learning |
| url | https://arxiv.org/abs/2403.01158 |